Identification of a Chitooligosaccharide Mechanism against Bacterial Leaf Blight on Rice by In Vitro and In Silico Studies

This study focuses on a commercial plant elicitor based on chitooligosaccharides (BIG^®), which aids in rice plant growth and disease resistance to bacterial leaf blight (BLB). When the pathogen (Xoo) vigorously attacks rice that has suffered yield losses, it can cause damage in up to 20% of the plant. Furthermore, Xoo is a seed-borne pathogen that can survive in rice seeds for an extended period. In this study, when rice seeds were soaked and sprayed with BIG^®, there was a significant increase in shoot and root length, as well as plant biomass. Furthermore, BIG^®-treated rice plants showed a significant reduction in BLB severity of more than 33%. Synchrotron radiation-based Fourier transform infrared (SR-FTIR) analysis was used to characterize BIG^®’s mechanism in the chemical structure of rice leaves. The SR-FTIR results at 1650, 1735, and 1114 cm⁻¹ indicated changes in biochemical components such as pectins, lignins, proteins, and celluloses. These findings demonstrated that commercial BIG^® not only increased rice growth but also induced resistance to BLB. The drug’s target enzyme, Xoo 1075 from Xanthomonas oryzae (PDB ID: 5CY8), was analyzed for its interactions with polymer ingredients, specifically chitooligosaccharides, to gain molecular insights down to the atomic level. The results are intriguing, with a strong binding of the chitooligosaccharide polymer with the drug target, revealing 10 hydrogen bonds between the protein and polymer. Overall, the computational analysis supported the experimentally demonstrated strong binding of chitooligosaccharides to the drug target.     

Comparative evaluation of physicochemical,functional and texture properties and sensory acceptance of different instant rice varieties coated with Spirulina and edible polymers

This study aimed to determine the influence of Spirulina platensis and edible polymer coating on the physicochemical and texture properties, and sensory acceptance of coated instant rice: glutinous white rice (GWR), glutinous black rice (GBR), non-glutinous white rice (NWR) and non-glutinous black rice (NBR). Instant rice was cooked and dried, and then coated by a pan-coating process. Black rice varieties (GBR and NBR) showed higher amounts of protein, fibre, ash and bioactive compounds than white rice varieties (GWR and NWR). The Spirulina-coated rice had significantly improved texture properties, especially black rice cultivars. The hedonic rating of the coated instant rice ranged from 5 (neither like nor dislike) to 6 (slightly liked), and GBR obtained a higher acceptability score (6.93). Principal component analysis models allowed sample classification. Therefore, fortification with Spirulina as a coating on instant rice could be an alternative way to produce functional instant rice with extra health benefits.     

A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems

The job-shop scheduling problem is one of the most difficult production planning problems. Since it is in the NP-hard class, a recent trend in solving the job-shop scheduling problem is shifting towards the use of heuristic and metaheuristic algorithms. This paper proposes a novel metaheuristic algorithm, which is a modification of the genetic algorithm. This proposed algorithm introduces two new concepts to the standard genetic algorithm: (1) fuzzy roulette wheel selection and (2) the mutation operation with tabu list. The proposed algorithm has been evaluated and compared with several state-of-the-art algorithms in the literature. The experimental results on 53 JSSPs show that the proposed algorithm is very effective in solving the combinatorial optimization problems. It outperforms all state-of-the-art algorithms on all benchmark problems in terms of the ability to achieve the optimal solution and the computational time.     

A new algorithm for flexible job-shop scheduling problem based on particle swarm optimization

This paper proposes a new algorithm, named EPSO, for solving flexible job-shop scheduling problem (FJSP) based on particle swarm optimization (PSO). EPSO includes two sets of features for expanding the solution space of FJSP and avoiding premature convergence to local optimum. These two sets are as follows: (I) particle life cycle that consists of four features: (1) courting call—increasing the number of more effective offspring (new solutions), (2) egg-laying stimulation—increasing the number of offspring from the better parents (current solutions), (3) biparental reproduction—increasing the diversity of the next generation (iteration) of solutions, and (4) population turnover—succeeding the population (the current set of all solutions) in the previous generation by a population in a new generation that is as able but more diverse than the previous one; and (II) discrete position update mechanism—moving particles (solutions) towards the flight leader (the best solution), namely, interchanging some integers in every solution with those in both the best solution and itself, using similar swarming strategy as the update procedure of the continuous PSO. The basic objective function used was to minimize makespan which is the most important objective, hence, providing the simplest way to measure the effectiveness of the generated solutions. Benchmarking EPSO with 20 well-known benchmark instances against two widely-reported optimization methods demonstrated that it performed either equally well or better than the other two.